Additive genetic variation in Pinus radiata bark chemistry and the chemical traits associated with variation in mammalian bark stripping

Machine learning methods in near infrared spectroscopy for predicting sensory traits in sweetpotatoes

It has been established that near infrared (NIR) spectroscopy has the potential of estimating sensory traits given the direct spectral responses that these properties have in the NIR region. In sweetpotato, sensory and texture traits are key for improving acceptability of the crop for food security and nutrition. Studies have statistically modelled the levels of NIR spectroscopy sensory characteristics using partial least squares (PLS) regression methods. To improve prediction accuracy, there are many advanced techniques, which could enhance modelling of fresh (wet and un-processed) samples or nonlinear dependence relationships. Performance of different quantitative prediction models for sensory traits developed using different machine learning methods were compared. Overall, results show that linear methods; linear support vector machine (L-SVM), principal component regression (PCR) and PLS exhibited higher mean R2 values than other statistical methods. For all the 27 sensory traits, calibration models using L-SVM and PCR has slightly higher overall R2 (x¯ = 0.33) compared to PLS (x¯ = 0.32) and radial-based SVM (NL-SVM; x¯= 0.30). The levels of orange color intensity were the best predicted by all the calibration models (R2 = 0.87 - 0.89). The elastic net linear regression (ENR) and tree-based methods; extreme gradient boost (XGBoost) and random forest (RF) performed worse than would be expected but could possibly be improved with increased sample size. Lower average R2 values were observed for calibration models of ENR (x¯ = 0.26), XGBoost (x¯ = 0.26) and RF (x¯ = 0.22). The overall RMSE in calibration models was lower in PCR models (X = 0.82) compared to L-SVM (x¯ = 0.86) and PLS (x¯ = 0.90). ENR, XGBoost and RF also had higher RMSE (x¯ = 0.90 - 0.92). Effective wavelengths selection using the interval partial least-squares regression (iPLS), improved the performance of the models but did not perform as good as the PLS. SNV pre-treatment was useful in improving model performance.

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

BACKGROUND

Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce.

RESULTS

Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors.

CONCLUSIONS

The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.

Characterization of 35 Masson pine (Pinus massoniana) half-sib families from two provinces based on metabolite properties

Plant metabolism is an important functional trait, and its metabolites have physiological and ecological functions to adapt to the growth environment. However, the physiological and ecological functions of metabolites from different provinces of the same plant species are still unclear. Therefore, this study aimed to determine whether metabolites from different provinces of Masson pine (Pinus massoniana Lamb.) have the corresponding metabolic traits. The gas chromatography–mass spectrometry technique and metabonomic analysis methods were used to characterize 35 Masson pine half-sib families from two provinces. A total of 116 metabolites were putatively identified in 35 families of Masson pine, among which the average content of organic acids was the highest, followed by saccharides and alcohols, and phosphoric acids. Comparative analysis of metabolite groups showed that organic acids, amines, and others were significantly different between the Masson pine families from Guangxi and Guizhou provinces. Six differential metabolites were found between the provinces from Guizhou and Guangxi, namely caffeic acid, L-ascorbic acid, gentiobiose, xylitol, d-pinitol, and β-sitosterol. The most significantly enriched pathways among differentially expressed metabolites between the two provinces were steroid biosynthesis, phenylpropanoid biosynthesis, glutathione metabolism, pentose and glucuronate interconversions. Overall, the results showed that Masson pine half-sib families from different geographical provinces have different metabolite profiles and their metabolites are affected by geographical provenance and growth environment adaptability. This study revealed that the breeding of Masson pine families from different provinces changed the metabolite profiles, providing a reference for the multipurpose breeding of Masson pine.

Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height, and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (genomic best linear unbiased prediction), multitrait single-step genomic best linear unbiased prediction, which integrated the marker-based and pedigree-based relationship matrices (single-step genomic best linear unbiased prediction) and the single-trait generalized ridge regression-were compared to equivalent single- or multitrait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the single-step genomic best linear unbiased prediction was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of generalized ridge regression did not markedly improve predictive ability over genomic best linear unbiased prediction, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or the presence of outliers affected the genomic or pedigree-based genetic estimates.

Phenoplasticity of Essential Oils from Two Species of Piper (Piperaceae): Comparing Wild Specimens and Bi-Generational Monoclonal Cultivars

This study tested the hypothesis that “clonal chemical heritability is a crucial factor for the conservation of chemical uniformity of Piper essential oils in controlled monoclonal cultivation”. We asexually propagated first and second-generation clones of two medicinal and aromatic species, Piper gaudichaudianum Kunth and Piper mollicomum Kunth (Piperaceae), for use as experimental models since they show high chemical plasticity in the wild. Leaves from wild specimens of both species, and their respective cultivated specimens, were hydrodistilled in a Clevenger-type apparatus to produce essential oils (EOs). EOs were chemically characterised by GC-MS and GC-FID. The analysis identified 63 compounds in EO of P. mollicomum, which were predominantly monoterpenes, and 59 in EO of P. gaudichaudianum, which were predominantly sesquiterpenes. Evaluation of chemical diversity and oxi-reduction indices showed a loss of chemical homology across the intergenerational cline. Chemometric analysis indicated higher chemical plasticity between wild and intergenerational specimens of P. mollicomum, than for P. gaudichaudianum. EO compounds were significantly less oxidized throughout the generations in both species. Therefore, while clonal heritability is crucial to chemical homology, significant chemical plasticity is likely to occur when cultivated from wild specimens.

Analysis of the transcriptome of the needles and bark of Pinus radiata induced by bark stripping and methyl jasmonate

BACKGROUND

Plants are attacked by diverse insect and mammalian herbivores and respond with different physical and chemical defences. Transcriptional changes underlie these phenotypic changes. Simulated herbivory has been used to study the transcriptional and other early regulation events of these plant responses. In this study, constitutive and induced transcriptional responses to artificial bark stripping are compared in the needles and the bark of Pinus radiata to the responses from application of the plant stressor, methyl jasmonate. The time progression of the responses was assessed over a 4-week period.

RESULTS

Of the 6312 unique transcripts studied, 86.6% were differentially expressed between the needles and the bark prior to treatment. The most abundant constitutive transcripts were related to defence and photosynthesis and their expression did not differ between the needles and the bark. While no differential expression of transcripts were detected in the needles following bark stripping, in the bark this treatment caused an up-regulation and down-regulation of genes associated with primary and secondary metabolism. Methyl jasmonate treatment caused differential expression of transcripts in both the bark and the needles, with individual genes related to primary metabolism more responsive than those associated with secondary metabolism. The up-regulation of genes related to sugar break-down and the repression of genes related with photosynthesis, following both treatments was consistent with the strong down-regulation of sugars that has been observed in the same population. Relative to the control, the treatments caused a differential expression of genes involved in signalling, photosynthesis, carbohydrate and lipid metabolism as well as defence and water stress. However, non-overlapping transcripts were detected between the needles and the bark, between treatments and at different times of assessment. Methyl jasmonate induced more transcriptional responses in the bark than bark stripping, although the peak of expression following both treatments was detected 7 days post treatment application. The effects of bark stripping were localised, and no systemic changes were detected in the needles.

CONCLUSION

There are constitutive and induced differences in the needle and bark transcriptome of Pinus radiata. Some expression responses to bark stripping may differ from other biotic and abiotic stresses, which contributes to the understanding of plant molecular responses to diverse stresses. Whether the gene expression changes are heritable and how they differ between resistant and susceptible families identified in earlier studies needs further investigation.